I am currently the Chief AI Officer at Axiomatic AI Institute, working on novel AI techniques, that are interpretable and verifiable, specifically designed for scientific and engineering applications. My research interests lie at the intersection of physics and artificial intelligence. In my work, I have been investigating how machine learning can be used to support scientific discovery, exploring how a future "Artificial Scientist" could automatically learn from observations, understand relevant concepts, build new physical models and design new experiments similar to how human scientists do. Previously, I was a Flatiron Research Fellow at the Flatiron Institute, in the Foundation Models for Science initiative. I received my Ph.D. in Physics from the Friedrich-Alexander Universität in Erlangen, Germany, in association with the Max Planck Institute for the Science of Light. In the past, I earned my B.Sc. and M.Sc. degrees in Physics at Sapienza University of Rome, Italy, focusing on the statistical physics of spin glasses.
At Axiomatic AI, I investigate the use of state-of-the-art techniques in artificial intelligence (reinforcement learning, agentic LLMs, program synthesis) to facilitate the development process of photonic integrated circuits. Stay tuned!
At Polymathic AI, I have been exploring how to leverage scientific data from multiple sources to build AI models more generally suited for scientific applications, specifically in astrophysics. I have been working on AstroCLIP, a model that aligns galactic images and spectra, and on a Multimodal Observation Model for Astrophysics (under review).
One of the most useful concepts in physics is the notion of collective coordinates. However, it is not clear a-priori which low-dimensional function is best suited as a compact description of the high dimensional data. We investigated a technique to find these quantities based on their information content. We generalized the idea of mutual information, a solid concept in information theory, to work for this purpose, i.e. to quantify the amount of information between a random variable and a deterministic continuous function of it. In addition, we showed that it may be possible to also automatically extract collective variables by parametrizing features and optimizing over the parameters.
Bayesian experimental design allows to efficiently select measurements to characterize a physical system, by maximizing the expected information gain. Recent developments in deep neural networks and normalizing flows allow for a more efficient approximation of the posterior and thus the extension of this technique to complex high-dimensional situations. We investigated how this approach holds promise for adaptive measurement strategies to characterize present-day quantum many-body platforms. We focused on arrays of coupled cavities and qubit arrays, which both represent model systems of high relevance for modern applications, like quantum simulations and computing, and both have been realized in platforms where measurement and control can be exploited to characterize and counteract unavoidable disorder.
Despite rapid progress in the field, it is still challenging to discover new ways to take advantage of quantum computation: all quantum algorithms need to be designed by hand, and quantum mechanics is notoriously counterintuitive. We studied how AI, in the form of program synthesis, may help to overcome some of these difficulties, by showing how a computer can incrementally learn concepts relevant for quantum circuit synthesis with experience, and reuse them in unseen tasks. In particular, we focused on the decomposition of unitary matrices into quantum circuits, and showed how, starting from a set of elementary gates, we can automatically discover a library of new useful composite gates and use them to decompose more and more complicated unitaries.
Scientists are agents that performs experiments to improve their model of the world. Their reward is mainly due to the observation of some new phenomenon and the improvement of their scientific model. In Reinforcement Learning, a similar situation happens when the reward is sparse, and it is useful to design strategies to explore the environment independently. Curiosity-driven and novelty-based techniques allow to intrinsically motivate the agent to explore the environment, in a similar way as a human scientist would do. We developed a particular novelty-based techniques, suitable for challenging exploration tasks like 3D-environments and Atari video games.
Some less recent work:
The device-independent approach for the characterization of a quantum system allows to draw conclusions about a system without the need of a precise knowledge of the measurement apparatus. We showed an implementation of this framework for the characterization of a quantum channel in a photonic setup.
Finite-size corrections to the free energy of the Sherrington-Kirkpatrick spin glass in the low-temperature phase are a long-standing and still open problem. We investigated the role of one contribution to these corrections, the so-called longitudinal fluctuations, and determined its scaling exponent both with an analytical and numerical approach.
I usually commit to open source the research code. Please visit my GitHub page for the updated list.
| Name | Description |
|---|---|
| Polymathic AI | In this repository, we release the code of the Foundation Models for Science projects and related works |
| unitary-synthesis | Discovering quantum circuit components with program synthesis |
| active-learning | Bayesian experimental design for quantum many-body systems |
| rmi | Renormalized mutual information for artificial scientific discovery |